scholarly journals Screening tool for identification of hip fractures in the prehospital setting

2021 ◽  
Vol 4 (4) ◽  
pp. e157
Author(s):  
Danielle M. Gillette ◽  
Olivia Cheng ◽  
Alghin Wilson ◽  
Rogerio Mantero ◽  
Douglas Chisholm ◽  
...  
CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S79-S80 ◽  
Author(s):  
S. AlQahtani ◽  
P. Menzies ◽  
B. Bigham ◽  
M. Welsford

Introduction: Early recognition of sepsis is key in delivering timely life-saving interventions. The role of paramedics in recognition of these patients is understudied. It is not known if the usual prehospital information gathered is sufficient for severe sepsis recognition. We sought to: 1) evaluate the paramedic medical records (PMRs) of severe sepsis patients to describe epidemiologic characteristics; 2) determine which severe sepsis recognition and prediction scores are routinely captured by paramedics; and 3) determine how these scores perform in the prehospital setting. Methods: We performed a retrospective review of patients ≥18 years who met the definition of severe sepsis in one of two urban Emergency Departments (ED) and had arrived by ambulance over an eighteen-month period. PMRs were evaluated for demographic, physiologic and clinical variables. The information was entered into a database, which auto-filled a tool that determined SIRS criteria, shock index, prehospital critical illness score, NEWS, MEWS, HEWS, MEDS and qSOFA. Descriptive statistics were calculated. Results: We enrolled 298 eligible sepsis patients: male 50.3%, mean age 73 years, and mean prehospital transportation time 30 minutes. Hospital mortality was 37.5%. PMRs captured initial: respiratory rate 88.6%, heart rate 90%, systolic blood pressure 83.2%, oxygen saturation 59%, temperature 18.7%, and Glasgow Coma Scale 89%. Although complete MEWS and HEWS data capture rate was <17%, 98% and 68% patients met the cut-point defining “critically-unwell” (MEWS ≥3) and “trigger score” (HEWS ≥5), respectively. The qSOFA criteria were completely captured in 82% of patients; however, it was positive in only 36%. It performed similarly to SIRS, which was positive in only 34% of patients. The other scores were interim in having complete data captured and performance for sepsis recognition. Conclusion: Patients transported by ambulance with severe sepsis have high mortality. Despite the variable rate of data capture, PMRs include sufficient data points to recognize prehospital severe sepsis. A validated screening tool that can be applied by paramedics is still lacking. qSOFA does not appear to be sensitive enough to be used as a prehospital screening tool for deadly sepsis, however, MEWS or HEWS may be appropriate to evaluate in a large prospective study.


2020 ◽  
Vol 37 (9) ◽  
pp. 587-588
Author(s):  
Ian Gibbons ◽  
Owen Williams

A short cut review was carried out to whether the FAST screening tool is more accurate than the ROSIER tool at correctly identifying those with stroke in the prehospital setting. 9 papers presented the best evidence to answer the clinical question. The author, date and country of publication, patient group studied, study type, relevant outcomes, results and study weaknesses of these papers are tabulated. It is concluded that FAST and ROSIER have similar sensitivities in the recognition of stroke, with ROSIER demonstrating a higher specificity in the prehospital setting.


2009 ◽  
Vol 18 (4) ◽  
pp. 129-133 ◽  
Author(s):  
Kelly Poskus

Abstract The bedside swallow screen has become an essential part of the evaluation of a patient after stroke in the hospital setting. Implementing this type of tool should be simple. However, reinforcement and monitoring of the tool presents a challenge. Verifying the consistency and reliability of nurses performing the bedside swallow screen can be a difficult task. This article will document the journey of implementing and maintaining a reliable and valid nursing bedside swallow screen.


2012 ◽  
Vol 45 (18) ◽  
pp. 2
Author(s):  
PATRICE WENDLING
Keyword(s):  

Author(s):  
Douglas L. Epperson ◽  
James D. Kaul ◽  
Stephen Huot ◽  
Robin Goldman ◽  
Will Alexander
Keyword(s):  

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